Sentiment analysis is an area of study that is extensively researched. It is concerned with the process of recognizing and classifying opinions that are conveyed in text into one of three categories: positive, negative, or neutral polarities. One of the most important steps in improving the accuracy of sentiment classification is feature selection, which is a process that helps reduce dimensionality, eliminate features that are irrelevant, and improve the efficiency of the model. This study explores the performance of a variety of feature selection methods for sentiment analysis, with a special emphasis on hybrid wrapper-based approaches. In order to create a feature vocabulary that is as efficient as possible, SVM- Recursive Feature Elimination (RFE) is followed by Sequential Feature Selection (SFS/SBS) in this particular case. The efficacy of these selected characteristics is evaluated with the use of a number of machine learning classifiers, such as Support Vector Machines (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Decision Tree (DT). In addition, in order to further improve the classification performance, ensemble learning approaches, including as Random Forests, Extra-Trees, XGBoost, AdaBoost, and stacking, are used. The findings of the experiment show that the hybrid feature selection strategy, when used in conjunction with ensemble methods, leads to a considerable improvement in sentiment classification accuracy. As a result, a solid foundation for opinion mining in text-based data is provided.